Smooth global and local path planning for mobile robot using particle swarm optimization, radial basis functions, splines and Bézier curves

An approach to plan smooth paths for mobile robots using a Radial Basis Function (RBF) neural network trained with Particle Swarm Optimization (PSO) was presented in [1]. Taking the previous approach as an starting point, in this paper it is shown that it is possible to construct a smooth simple global path and then modify this path locally using PSO-RBF, Ferguson splines or Bézier curves trained with PSO, in order to describe more complex paths in partially known environments. Experimental results show that our approach is fast and effective to deal with complex environments.